//Copyright (C) 2005 Richard J. Northedge
//
// This library is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 2.1 of the License, or (at your option) any later version.
//
// This library is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
// GNU Lesser General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License along with this program; if not, write to the Free Software
// Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
//This file is based on the ChunkerME.java source file found in the
//original java implementation of OpenNLP. That source file contains the following header:
//Copyright (C) 2003 Thomas Morton
//
//This library is free software; you can redistribute it and/or
//modify it under the terms of the GNU Lesser General Public
//License as published by the Free Software Foundation; either
//version 2.1 of the License, or (at your option) any later version.
//
//This library is distributed in the hope that it will be useful,
//but WITHOUT ANY WARRANTY; without even the implied warranty of
//MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
//GNU Lesser General Public License for more details.
//
//You should have received a copy of the GNU Lesser General Public
//License along with this program; if not, write to the Free Software
//Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
using System;
using System.Collections;
namespace OpenNLP.Tools.Chunker
{
/// <summary>
/// This class represents a maximum-entropy-based chunker. Such a chunker can be used to
/// find flat structures based on sequence inputs such as noun phrases or named entities.
/// </summary>
public class MaximumEntropyChunker : IChunker
{
private Util.BeamSearch mBeam;
private Util.Sequence mBestSequence;
private SharpEntropy.IMaximumEntropyModel mModel;
/// <summary>
/// The beam used to search for sequences of chunk tag assignments.
/// </summary>
protected internal Util.BeamSearch Beam
{
get
{
return mBeam;
}
}
/// <summary>
/// The model used to assign chunk tags to a sequence of tokens.
/// </summary>
protected internal SharpEntropy.IMaximumEntropyModel Model
{
get
{
return mModel;
}
}
/// <summary>
/// Creates a chunker using the specified model.
/// </summary>
/// <param name="model">
/// The maximum entropy model for this chunker.
/// </param>
public MaximumEntropyChunker(SharpEntropy.IMaximumEntropyModel model):this(model, new DefaultChunkerContextGenerator(), 10)
{
}
/// <summary>
/// Creates a chunker using the specified model and context generator.
/// </summary>
/// <param name="model">
/// The maximum entropy model for this chunker.
/// </param>
/// <param name="contextGenerator">
/// The context generator to be used by the specified model.
/// </param>
public MaximumEntropyChunker(SharpEntropy.IMaximumEntropyModel model, IChunkerContextGenerator contextGenerator):this(model, contextGenerator, 10)
{
}
/// <summary>
/// Creates a chunker using the specified model and context generator and decodes the
/// model using a beam search of the specified size.
/// </summary>
/// <param name="model">
/// The maximum entropy model for this chunker.
/// </param>
/// <param name="contextGenerator">
/// The context generator to be used by the specified model.
/// </param>
/// <param name="beamSize">
/// The size of the beam that should be used when decoding sequences.
/// </param>
public MaximumEntropyChunker(SharpEntropy.IMaximumEntropyModel model, IChunkerContextGenerator contextGenerator, int beamSize)
{
mBeam = new ChunkBeamSearch(this, beamSize, contextGenerator, model);
mModel = model;
}
/// <summary>
/// Performs a chunking operation.
/// </summary>
/// <param name="tokens">
/// ArrayList of tokens
/// </param>
/// <param name="tags">
/// ArrayList of tags corresponding to the tokens
/// </param>
/// <returns>
/// ArrayList of results, containing a value for each token, indicating the chunk that that token belongs to.
/// </returns>
public virtual ArrayList Chunk(ArrayList tokens, ArrayList tags)
{
mBestSequence = mBeam.BestSequence(tokens, new object[] { (string[]) tags.ToArray(typeof(string)) });
return mBestSequence.Outcomes;
}
/// <summary>
/// Performs a chunking operation.
/// </summary>
/// <param name="tokens">
/// Object array of tokens
/// </param>
/// <param name="tags">
/// String array of POS tags corresponding to the tokens in the object array
/// </param>
/// <returns>
/// String array containing a value for each token, indicating the chunk that that token belongs to.
/// </returns>
public virtual string[] Chunk(object[] tokens, string[] tags)
{
mBestSequence = mBeam.BestSequence(new ArrayList(tokens), new object[]{tags});
ArrayList chunks = mBestSequence.Outcomes;
return (string[]) chunks.ToArray(typeof(string));
}
/// <summary>
/// Gets a list of all the possible chunking tags.
/// </summary>
/// <returns>
/// String array, each entry containing a chunking tag.
/// </returns>
public virtual string[] AllTags()
{
string[] tags = new string[mModel.OutcomeCount];
for (int currentTag = 0; currentTag < mModel.OutcomeCount; currentTag++)
{
tags[currentTag] = mModel.GetOutcomeName(currentTag);
}
return tags;
}
/// <summary>
/// This method determines wheter the outcome is valid for the preceding sequence.
/// This can be used to implement constraints on what sequences are valid.
/// </summary>
/// <param name="outcome">
/// The outcome.
/// </param>
/// <param name="sequence">
/// The preceding sequence of outcomes assignments.
/// </param>
/// <returns>
/// true if the outcome is valid for the sequence, false otherwise.
/// </returns>
protected internal virtual bool ValidOutcome(string outcome, Util.Sequence sequence)
{
return true;
}
/// <summary>
/// This method determines wheter the outcome is valid for the preceeding sequence.
/// This can be used to implement constraints on what sequences are valid.
/// </summary>
/// <param name="outcome">
/// The outcome.
/// </param>
/// <param name="sequence">
/// The preceding sequence of outcomes assignments.
/// </param>
/// <returns>
/// true if the outcome is valid for the sequence, false otherwise.
/// </returns>
protected internal virtual bool ValidOutcome(string outcome, string[] sequence)
{
return true;
}
/// <summary>
/// This class implements the abstract BeamSearch class to allow for the chunker to use
/// the common beam search code.
/// </summary>
private class ChunkBeamSearch : Util.BeamSearch
{
private MaximumEntropyChunker mMaxentChunker;
public ChunkBeamSearch(MaximumEntropyChunker maxentChunker, int size, IChunkerContextGenerator contextGenerator, SharpEntropy.IMaximumEntropyModel model):base(size, contextGenerator, model)
{
mMaxentChunker = maxentChunker;
}
protected internal override bool ValidSequence(int index, ArrayList inputSequence, Util.Sequence outcomesSequence, string outcome)
{
return mMaxentChunker.ValidOutcome(outcome, outcomesSequence);
}
protected internal override bool ValidSequence(int index, object[] inputSequence, string[] outcomesSequence, string outcome)
{
return mMaxentChunker.ValidOutcome(outcome, outcomesSequence);
}
}
/// <summary>
/// Populates the specified array with the probabilities of the last decoded sequence. The
/// sequence was determined based on the previous call to <code>chunk</code>. The
/// specified array should be at least as large as the numbe of tokens in the previous call to <code>chunk</code>.
/// </summary>
/// <param name="probabilities">
/// An array used to hold the probabilities of the last decoded sequence.
/// </param>
public virtual void GetProbabilities(double[] probabilities)
{
mBestSequence.GetProbabilities(probabilities);
}
/// <summary>
/// Returns an array with the probabilities of the last decoded sequence. The
/// sequence was determined based on the previous call to <code>chunk</code>.
/// </summary>
/// <returns>
/// An array with the same number of probabilities as tokens were sent to <code>chunk</code>
/// when it was last called.
/// </returns>
public virtual double[] GetProbabilities()
{
return mBestSequence.GetProbabilities();
}
/// <summary>
/// Trains the chunker. Training file should be one word per line where each line consists of a
/// space-delimited triple of "word pos outcome". Sentence breaks are indicated by blank lines.
/// </summary>
/// <param name="eventReader">
/// The chunker event reader.
/// </param>
/// <returns>
/// Trained model.
/// </returns>
public static SharpEntropy.GisModel Train(SharpEntropy.ITrainingEventReader eventReader)
{
return Train(eventReader, 100, 5);
}
/// <summary>
/// Trains the chunker. Training file should be one word per line where each line consists of a
/// space-delimited triple of "word pos outcome". Sentence breaks are indicated by blank lines.
/// </summary>
/// <param name="eventReader">
/// The chunker event reader.
/// </param>
/// <param name="iterations">
/// The number of iterations to perform.
/// </param>
/// <param name="cutoff">
/// The number of times a predicate must be seen in order
/// to be relevant for training.
/// </param>
/// <returns>
/// Trained model.
/// </returns>
public static SharpEntropy.GisModel Train(SharpEntropy.ITrainingEventReader eventReader, int iterations, int cutoff)
{
SharpEntropy.GisTrainer trainer = new SharpEntropy.GisTrainer();
trainer.TrainModel(iterations, new SharpEntropy.TwoPassDataIndexer(eventReader, cutoff));
return new SharpEntropy.GisModel(trainer);
}
}
}

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About the Author

Richard Northedge is a senior developer with a UK Microsoft Gold Partner company. He has a postgraduate degree in English Literature, has been programming professionally since 1998 and has been an MCSD since 2000.